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EQ2330 Image and Video Processing 7.5 credits

Course memo Autumn 2024-50313

Version 1 – 10/27/2024, 6:57:24 PM

Course offering

Autumn 2024-50313 (Start date 27 Oct 2024, English)

Language Of Instruction

English

Offered By

EECS/Intelligent Systems

Course memo Autumn 2024

Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Autumn 2024

Content and learning outcomes

Course contents

This course introduces the principles of digital image and video processing, discusses current image and video processing technology, and provides hands-on experience with image/video processing and communication methods. The course includes topics on image filtering and restoration, image transform algorithms, multiresolution image processing, image matching and segmentation techniques, as well as image and video compression.

Intended learning outcomes

After passing this course, participants should be able to

  • describe and use the principles of digital image and video processing to develop image processing algorithms,
  • develop image processing algorithms for image filtering and restoration, image transformation and multiresolution processing, image and video compression, as well as image matching and segmentation,
  • implement (for example with MatLab) and assess the developed image processing algorithms, 
  • explain algorithm design choices using the principles of digital image/video processing,
  • develop image processing algorithms for a given practical image/video processing problem
  • analyze given image/video processing problems, identify and explain the challenges, propose possible solutions, and explain the chosen algorithm design.

To achive higher grades, participants should also be able to

  • solve more advanced problems in all areas mentioned above.

Learning activities

Individual preparation assignments, peer reviews, group projects, exercises, lectures.

Detailed plan

Learning activities Content Preparations
Lectures    
Exercises    
Individual preparation assignments   Prepare before exercise
Peer reviews of preparation assignments   During exercise session or afterwards, if exercise session cannot be attended
3 group projects   Group of 2-3 students; submit one report per project and group



Preparations before course start

Recommended prerequisites

EQ1220 Signal Theory or equivalent

Literature

Content is covered by lecture and exercise material.

For further reading, the following book is helpful: R. C. Gonzales, R.E. Woods, “Digital Image Processing,” Prentice-Hall.

Examination and completion

Grading scale

A, B, C, D, E, FX, F

Examination

  • INL1 - Assignment, 1.5 credits, Grading scale: P, F
  • PRO1 - Course projects, 3.0 credits, Grading scale: A, B, C, D, E, FX, F
  • TENA - Written exam, 3.0 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

The section below is not retrieved from the course syllabus:

Course projects will contribute to the final grade. In general, projects and written exam contribute equally to the final grade. The examiner reserves the right to adjust the final weighting at the end of the course.

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

No information inserted

Round Facts

Start date

28 Oct 2024

Course offering

  • Autumn 2024-50313

Language Of Instruction

English

Offered By

EECS/Intelligent Systems

Contacts

Course Coordinator

Teachers

Teacher Assistants

Examiner